As the transformation towards digital and cloud-first thinking continues to dominate, data has become one of the most valuable assets for all businesses. However, not all data is created equal, and some data types require more stringent security measures than others.
Plus, with massive cloud migration, organizations are harnessing diverse data types (intellectual property, financial, business confidential, and regulated PII/PCI/PHI data) in increasingly complex environments.
This is where data classification comes in. By categorizing data based on its level of sensitivity, organizations can protect their most valuable information from unauthorized access, disclosure, or modification.
When it comes to data protection, the first step is knowing how to identify and classify data.
A brief primer on data classification
Data classification helps you identify high-value data in your enterprise by categorizing it into an agreed set of specific and meaningful categories. Data classification drives multiple use cases such as data labeling, sensitive data identification, automating protection, compliance, security, access control, and data retention.
Basically speaking, data classification is the ability to label your data. When implemented effectively, data classification can be both a boost to security and also simplify decision-making processes. It allows organizations to allocate resources more efficiently, focus on protecting their most critical assets, and maintain compliance with evolving regulatory standards.
When businesses understand where high-value data resides and how it’s used, they can make the informed decisions they need to not only mitigate risks but support long-term operational goals.
What are data classification levels?
Data classification levels are categories used to define the sensitivity and risk associated with different types of data. By assigning levels, organizations can make sure appropriate security measures are applied based on the potential impact of unauthorized access or disclosure.
The four commonly used levels include:
Public
Public data is information that can be freely shared without any security restrictions. This type of data poses minimal risk if disclosed and often includes marketing materials, publicly available reports, and press releases.
- Examples of public data
- Company brochures
- Press announcements
- Blog posts
Risks and controls for public data: While public data is low-risk, organizations should still monitor its distribution to maintain accuracy and prevent misuse.
Internal
Internal data is intended for use within the organization and may contain proprietary or operational information. Though not highly sensitive, unauthorized disclosure could lead to minor operational disruptions or reputational damage.
- Examples of internal data
- Employee directories
- Internal project updates
- Operational procedures
Risks and controls for internal data: Organizations should implement access controls to restrict this data to employees and prevent accidental exposure.
Confidential
Confidential data is sensitive information that requires strong protections to prevent harm to the organization or its stakeholders. This includes financial records, contracts, and client information.
- Examples of confidential data
- Employee performance reviews
- Business contracts
- Customer account details
Risks and controls for confidential data: Unauthorized access to confidential data can lead to financial loss or legal implications. Encrypting files and implementing multi-factor authentication (MFA) are essential.
Highly confidential
Highly confidential data represents the most sensitive information that, if disclosed, could cause significant financial, legal, or reputational damage. Organizations must prioritize securing this type of data with advanced protections.
- Examples of highly confidential data:
- Intellectual property
- Mergers and acquisitions (M&A) documents
- Personally identifiable information (PII)
Risks and controls for highly confidential data: This level requires stringent measures such as restricted access, robust encryption, and continuous monitoring to prevent breaches.
It’s important to note that not all data needs to be classified at the same level. For example, customer names and addresses may be classified as “confidential,” while financial information such as credit card numbers may be classified as “highly confidential.” Proper data classification ensures that each type of data is protected according to its level of sensitivity.
Why is data classification important?
Data classification can be a great weapon in an organization’s arsenal for protecting sensitive information against cyber threats. Organizations risk exposing valuable information to issues like unauthorized access, disclosure, or modification if data is not properly classified.
In these cases, several negative consequences can occur, including financial loss, reputational damage, and legal liabilities.
Proper data classification allows organizations to:
Identify their most sensitive data: By categorizing data based on its level of sensitivity, organizations can pinpoint their most valuable and sensitive information and prioritize its protection accordingly.
Implement appropriate security measures: Once data has been classified, organizations can deploy any relevant security measures to protect it from cyber threats. For example, highly confidential data may require more robust encryption, access controls, and monitoring than data classified as public.
Comply with relevant regulations: Many regulations and standards, such as HIPAA, GDPR, and PCI-DSS, require organizations to classify their data and implement appropriate security measures based on its level of sensitivity. Non-compliance with these regulations can lead to fines, legal liabilities, and reputational damage.
Importance of classification labels
Classification labels are a critical part of effective data classification. They help organizations categorize and protect their data by providing a visual representation of its level of sensitivity.
Classification labels may include colors, text labels, and metadata tags.
Classification labels make data classification easier by providing organizations with:
Clear communication: A clear and easy-to-understand way to communicate the level of sensitivity of data to all personnel within an organization. This helps ensure that everyone understands the appropriate level of protection required for different types of data.
Consistency: Consistent application of security controls across different systems and applications. This is important for maintaining the integrity of the data and preventing unauthorized access or disclosure.
Compliance: Meet regulatory and compliance requirements. For example, HIPAA requires that covered entities classify their data as confidential or highly confidential and implement appropriate security measures based on the level of sensitivity.
Efficient management: Help organizations manage their data more efficiently. For example, they can use metadata tags to automate the classification of new data and apply appropriate security controls automatically.
How sensitivity labels help improve data classification
Sensitivity labels work in tandem with data classification levels to provide an additional layer of security and usability. These labels help users understand how to handle specific data and ensure that protections align with organizational policies.
For example, sensitivity labels can:
- Automatically apply encryption to sensitive files.
- Set restrictions on data sharing and printing.
- Track access and modifications for auditing purposes.
Organizations using platforms like Microsoft 365 can automate sensitivity labeling to simplify how security policies are enforced.
The importance of data classification, using real-world scenarios
These scenarios illustrate the critical role of data classification in protecting sensitive data, ensuring regulatory compliance, and enhancing the overall security posture of organizations across various industries.
Scenario 1: PHI in Healthcare
A healthcare provider managing hordes of Protected Health Information (PHI) data faces the dual-edged challenge of securing that sensitive patient data and ensuring compliance with the Health Insurance Portability and Accountability Act (HIPAA). By implementing a data classification system, the provider categorizes patient records as “Highly Confidential” and adopts robust encryption and access control measures. This proactive approach could prevent a potential data breach and streamline compliance with HIPAA, safeguarding patient privacy and the provider’s reputation.
Scenario 2: Inventory management in Retail
A retail company operates both online and physical stores, holding vast amounts of inventory data. To optimize its supply chain and protect against inventory leakage, the company implements a data classification system, labeling inventory data as “Internal.” This classification allows for better internal data sharing among purchasing, sales, and logistics teams while protecting sensitive inventory information from external threats. Their enhanced inventory management can lead to reduced overhead, improved stock levels, and a competitive edge in the market.
Scenario 3: GDP compliance in Finance
A multinational retail company processes massive stores of personal data from customers across the EU. To comply with the General Data Protection Regulation (GDPR), the company adopts a data classification strategy, labeling customer data according to sensitivity and implementing appropriate security controls based on the classification. This helps the company achieve GDPR compliance and also streamlines data handling processes, reducing the risk of data breaches and associated fines or penalties.
Scenario 4: Student data in Education
A university stores extensive records of student information, including contact details, enrollment status, and academic performance. To balance the need for accessibility among faculty and the protection of student privacy, the university classifies student records as “Confidential.” Access controls are implemented to ensure that only authorized faculty members can access specific types of student information, depending on their roles. This level of classification protects student privacy and allows the university to comply with educational privacy laws —promoting a secure and trusting educational environment.
Best practices for data classification
Effective data classification requires careful planning, implementation, and ongoing management.
Some of the best practices for effectively classifying your data include:
Identifying your data assets: The first step in effective data classification is to identify all the data assets that need to be classified. This includes all physical, digital and cloud data, structured and unstructured.
Defining your classification levels: Once you have identified your data assets, you need to define your classification levels based on the level of sensitivity of each type of data. Ensure to consider any regulatory requirements or industry standards that apply to your organization.
Assigning classification labels: Once classification levels are defined, you need to assign appropriate classification labels to each type of data — using metadata tags, text labels, or color codes.
Implementing appropriate security controls: After classifying your data, you need to implement appropriate security controls to protect it from cyber threats. This includes access controls, encryption, monitoring, and incident response procedures.
Training employees: Effective data classification requires buy-in from all employees within an organization, including the C-suite. Make sure to provide training on data classification policies and procedures to ensure everyone understands their roles and responsibilities.
Regular reviews and updates: Data classification is an ongoing process. Review your data classification policies and procedures as often as possible to ensure they are still effective and up to date.
The most effective data classification strategy
It’s crucial to note that while most classification methods are better than having none at all, most classification tactics — like end-user, centralized and metadata-driven — can be time-consuming and ineffective.
For best results, you should seek out solutions that use sophisticated machine learning technologies to autonomously scan and categorize data — from financial data to PII/PHI/PCI to intellectual property to confidential business information – wherever it is stored.
The best-of-breed solutions can autonomously identify data, learn how it’s used, and determine whether it’s at risk. Look for a solution that empowers you to know where your data is across unstructured or structured data repositories, email/ messaging applications, cloud or on-premises – all with semantic context.
By following these best practices, organizations can ensure that their sensitive information is classified and protected appropriately, reducing the risk of cyber threats and ensuring compliance with relevant regulations.
